By viewing large-language AI models through the lens of harm-reduction thinking, healthcare adopters can cultivate a responsible, ethical and optimal integration of the technology. 

President Donald Trump has crystallized his No. 1 priority for artificial intelligence. Unshockingly, it’s “winning the AI race.” 

AI can improve the quality of remotely delivered care while simultaneously defending privacy and security for the telehealth patient. 

Gen Xers and their elders tend to believe AI will do more harm than good. More than half of American adults 50 and older place themselves in that somewhat cynical category. 

AI can hurt or help the cause of advancing equality of resources, services and outcomes in healthcare. If it’s to do more helping than hurting, the technology must permeate primary care—and do so with certain goals and guidelines. 

The numbers from Pittsburgh are staggering. And they’re all about blasting American-made AI into the stratosphere, if not to Infinity and beyond. 

In hospital settings, the success of AI adoption hinges on how well implementation leaders balance technological innovation in departmental silos with operational nimbleness across the enterprise. In a term, the latter refers to hospital logistics. 

In his drive to keep China from sprinting ahead of the U.S. in the AI arms race, President Trump may be willing to cut some risky corners.